June 13, 2023
10,000+ traits with RNA sequencing and mass spectrometry
Quantitative trait locus mapping identifies genetic loci that affect measurable traits
Multiparental populations offer high-resolution QTL mapping
New analysis tools, such as a pleiotropy test for multiparental populations, are needed
Insights into genetic architecture
Tool for expression trait hotspot dissection
Complements mediation analysis
Two-parent crosses
Applies to two traits that co-map
\(H_0\): Pleiotropy
\(H_A\): Two separate QTL
Perform a two-dimensional two-QTL scan
\(vec(Y) = Xvec(B) + vec(E)\)
Calculate likelihood at each ordered pair of positions
Calculate likelihood ratio test statistic
Multivariate random effects
Fixed effect for each founder allele
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.pull-right[.small[Photo by UNC Computational Genetics]]
Perform a two-dimensional two-QTL scan
\(vec(Y) = Xvec(B) + vec(G) + vec(E)\)
Calculate likelihood at each ordered pair of positions
Calculate likelihood ratio test statistic
Test statistic: \[- \log_{10} \frac{\max (\text{likelihood under pleiotropy})}{\max (\text{likelihood for separate QTL})}\]
Parametric bootstrap to get a \(p\)-value
Logan et al. (2013) and Recla et al. (2014) studied 261 Diversity Outbred mice
Measured about two dozen behavioral traits
Two traits map to Chr 8:
“hot plate latency” (57 cM)
“percent time in light” (55 cM)
\[LOD(\lambda_1, \lambda_2) = ll_{10}(\lambda_1, \lambda_2) - \max_{\lambda} ll_{10}(\lambda, \lambda)\]
\[\text{profile LOD}_{\text{trait 1}}(\lambda_1) = \max_{\lambda_2}LOD(\lambda_1, \lambda_2)\]
\[LOD_p(\lambda) = ll_{10}(\lambda, \lambda) - \max_{\lambda} ll_{10}(\lambda, \lambda)\]
\(\log_{10} \Lambda = 1.2\)
\(p = 0.11\) (1000 bootstrap samples)
Functions for \(d\)-variate, \(d\)-QTL scan & profile LOD plots
Uses C++ for matrix calculations (via Rcpp and RcppEigen)
Uses gemma2 R implementation of GEMMA EM algorithm for multivariate random effects
Unit tests, vignettes, and version control
Jiang, C. and Z. Zeng (1995). “Multiple trait analysis of genetic mapping for quantitative trait loci.” In: Genetics 140.3, pp. 1111-1127.
Logan, R. W., R. F. Robledo, et al. (2013). “High-precision genetic mapping of behavioral traits in the diversity outbred mouse population”. In: Genes, Brain and Behavior 12.4, pp. 424-437.
Recla, J. M., R. F. Robledo, et al. (2014). “Precise genetic mapping and integrative bioinformatics in Diversity Outbred mice reveals Hydin as a novel pain gene”. In: Mammalian genome 25.5-6, pp. 211-222.